Accessible Interface Project


NICU Scenario Video:
This wearable device interfaces with Cochlear Implants via bluetooth to adjust microphones to optimal settings thus facilitating small group conversation. The ML powered interface continually optimizes the experience based upon user feedback.
Designers, MGD Students: Alysa Buchanan, Matt Babb, Ellis Anderson.

Here-U Scenario Video:
Navigate, Identify, Categorize, Organize (NICO): These cleaning glasses use machine learning to detect and identify stains and spills in the kitchen. Via a conversational interface that speaks privately to the user through bone conduction, the glasses alert BVI users of messes and assists them with cleanup.

Designers, MGD Students: Shadrick Addy, Jessye Holmgren-Sidell, Matt Lemmond, Krithika Sathyamurthy.

Hi-fi interface designs for Here-U. Designers, MGD Students: Shadrick Addy, Jessye Holmgren-Sidell, Matt Lemmond, Krithika Sathyamurthy.

Level: , ,
Duration: 6 weeks
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Bookmark Project

Project Brief

Over the last 50 years we have worked to create interfaces that meet the needs of most—but not all—users. How can machine learning change this? Predictive algorithms enable computers to observe unstructured data and make decisions in a manner similar to humans. How can we harness this new technology to rethink interface design to meet the needs of all individuals? How might we use machine learning to not only lower barriers to access, but also make the experience delightful?

Design Prompt: Design an interface that harnesses the capabilities of Watson product/s to address a range of impairment as a blind/visually impaired (BVI) or a deaf/hard of hearing (DDH) user completes a specific task. Create a hi-fi prototype of interface and scenario video that demonstrates use of the interface in context.

Questions to Consider:

  • How might technology help to positively transform user experience rather than replacing/replicating “normal” seeing or hearing? (In other words, per our classroom discussion, “How can the designer provide access rather than try to ‘fix’ people.”)
  • In words of design researcher Sara Hendren: “What can a body do? Who might use which thing for what? Where might the surprises be? How might a familiar thing morph into something else altogether?”
  • How might your interface learn over time, optimizing the experience based on changing user needs? Remember, not only does impairment radically change between one user and the next, but an individual’s needs can shift from one day to the next and from one context to the next.


The Design Process:

  • Matrix Exercise: mapping Watson tools to problematic tasks for users
  • Benchmarking and User Interviews
  • Personas and Scenarios
  • User Journey Map of current user experience
  • Ideation: What If Exercise to explore possibilities improv style
  • Sketches and Roughs
  • Storyboard of User Experience
  • Crits
  • User Testing
  • Revised User Journey Maps
  • Hi-fi Prototypes
  • Scenario Videos and Final Presentations

Learning Objectives

  • Apply methods for rapid prototyping. Make, make, and make some more.
  • Apply user-centered inclusive design methods.
  • Acquire introductory knowledge of emerging interfaces.
  • Gain introductory knowledge of machine learning.


Hi-fi prototype: an interactive testable prototype. (The form of this prototype depends upon your concept)
Scenario video: A scenario video demonstrates the product in use from the perspective of the persona.


P 1-14 of the introduction to the book Feminist, Queer, Crip by Alison Kafer. (Great explanation of the current disability landscape. Defines and discusses medical models of disability and social models.)

Being Deaf is Unlearning “Paper Face” by Mel Chua

The Radical Challenge of Building a Dorm for the Deaf by Liz Stinson

“I’m Not Your Inspiration,” Ted Talk by Stella Young

Listen to: 99% Invisible: On Average

What is Machine Learning? by Karen Has, MIT Tech Review

Excerpt from Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil

(Note: there are additional resources on my website


The students were very engaged with this project. Each student found a personal connection to disability via a family member or friend that informed their work. They were able to dig deeply into user needs and apply research methods with which they were previously unfamiliar. Although the final results were only prototypes, the product concepts were technically viable. Members of the IBM Watson Health Team collaborated on the project as voluntary advisors. They participated in critiques and were able to bring their expertise into the discussion. This was not a sponsored studio project.

Although the project was only 6 weeks, we spent time early in the semester exploring the concepts behind machine learning and its implications on society. This is a juicy topic for generating classroom discussion. The resulting cultural discussions around privacy, surveillance and advocacy paired well with a deep dive into designing for disability.

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